Fingerprint-based human authentication has shown great potential for civil, forensic and corporate security applications in recent years. For large-scale databases, the complexity of the identification system increases and implementing these systems on general-purpose sequential computing devices becomes challenging. Field Programmable Gate Array (FPGA) has demonstrated to be an efficient tool for

Scheduling plays an important role in improving the performance of big data-parallel processing. Spark is an in-memory parallel computing framework that uses a multi-threaded model in task scheduling. Most Spark task scheduling processes do not take the memory into account, but the number of concurrent task threads determined by the user. It emerges a potential limitation for the performance. To overcome

This special issue assembles a set of 11 papers, which provide deep research results to report the advance of security and privacy technology in social big data. This preface provides overview of all articles in the viewpoint set.

Edge-cloud services provide heterogeneous virtual machine types to run various IoT workloads. Choosing the appropriate VM configuration for each workload can effectively improve performance and reduce costs. This article proposes ARVMEC, Adaptive Recommendation of Virtual Machines for IoT in Edge-Cloud Environment, which can always provide users with the best VM recommendation according to their own

Stream processing systems are increasingly becoming a core element in the data processing stack of many large companies, where they complement data management frameworks to build comprehensive solutions for processing, storage, and query. The adoption of separate tools leads to complex architectures that leave developers with the difficult task of writing application-specific code that ensures integration

The Internet of Things (IoT) is the core infrastructure of the smart city information system. With the explosive growth of IoT devices, how to securely maintain the important data generated by IoT devices has become an important issue. In the conventional IoT-cloud based infrastructure, the sensitive IoT data was stored in a third cloud service provider. However, in such a manner, the private IoT data

Efficient resource utilization becomes a major concern as large-scale distributed computing infrastructures keep growing in size. Malleability, the possibility for resource managers to dynamically increase or decrease the amount of resources allocated to a job, is a promising way to save energy and costs. However, state-of-the-art parallel and distributed storage systems have not been designed with

Many scientific and engineering applications necessitate computing the minimum norm solution of a sparse underdetermined linear system of equations. The minimum 2-norm solution of such systems can be obtained by a recent parallel algorithm, whose numerical effectiveness and parallel scalability are validated in both shared- and distributed-memory architectures. This parallel algorithm assumes the coefficient

The protection routing uses the multi-paths technique for integrating route discovery and route maintenance mechanisms in a network, and thus it can tolerate the failure of one component (including a node or a link). Tapolcai (2013) proposed a method showing that a network possessing two completely independent spanning trees (CISTs for short) suffices to configure a protection routing. However, it

The vector clock is a fundamental tool for tracking causality in distributed applications. Unfortunately, it does not scale well to large systems because each process needs to maintain a vector of size n, where n is the total number of processes in the system. To address this problem, we propose the prime clock, which is based on the encoding of the vector clock using prime numbers and uses a single

The state-of-the-art finite volume/difference magnetohydrodynamics (MHD) code Block Adaptive Tree Solarwind Roe Upwind Scheme (BATS-R-US) was originally designed with pure MPI parallelization. The maximum problem size achievable was limited by the storage requirements of the block tree structure. To mitigate this limitation, we have added multi-threaded OpenMP parallelization to the previous pure MPI

Basic Linear Algebra Subprograms (BLAS) are the building blocks for various numerical algorithms and are widely used in scientific computations. However, some linear algebra applications need more precision than the standard double precision available in most existing BLAS libraries. In this paper, we implement and evaluate multiple-precision scalar and vector BLAS functions on graphics processing

An important concept in finite state machine based testing is synchronization which is used to initialize an implementation to a particular state. Usually, synchronizing sequences are used for this purpose and the length of the sequence used is important since it determines the cost of the initialization process. Unfortunately, the shortest synchronization sequence problem is NP-Hard. Instead, heuristics

With the growing needs of data across the world, it almost hard to live without data a day. The fog computing concept is aiming to change the scenarios created by Cloud Computing environments and also to make the data-centric clouds decentralized and localized. Fog devices which aims to be at a shorter distance with the user, access data from the cloud itself. Hence, exchanging of data between the

Computing platforms ranging from embedded systems to server blades comprise of multiple Systems-on-Chips (SoCs). Conventionally, communication between chips in these multichip platforms are realized using high-speed I/O modules over metal traces on a substrate. Due to the high-power consumption of I/O modules and non-scalable pitch of pins or solder bumps their bandwidth density and power consumption

Modern Multi-Processor System-On-Chips (MPSOC) are widely used especially in real-time embedded systems due to their high throughput and low per unit cost. However, bounded latency is vital to guarantee fast response as well as fairness for applications running on multicore processors. In this paper, a new Priority-base Memory Controller for Embedded Systems (PMSMC) that prioritizes concurrently running

While databases provide capabilities to enforce security and privacy policies, two major issues still prevent applications from safely delegating such policies to the database. The first one is the loss of user identity in multitiered environments which renders the database security features of little to no value. The second issue is the unsafe coexistence between the security capabilities and fundamental

Nested branches are common in applications with decision trees. The more layers in the branch nest, the larger slowdown is caused by nested branch divergence on GPU. Since inner branches are impractical to evaluate on host end, thread-data remapping via GPU shared memory is so far the most suitable solution. However, existing solution cannot handle inner branches directly due to undefined behavior

Performance variability is an important factor of high-performance computing (HPC) systems. HPC performance variability is often complex because its sources interact and are distributed throughout the system stack. For example, the performance variability of I/O throughput can be affected by factors such as CPU frequency, the number of I/O threads, file size, and record size. In this paper, we focus

Significant strides have been made in supervised learning settings thanks to the successful application of deep learning. Now, recent work has brought the techniques of deep learning to bear on sequential decision processes in the area of deep reinforcement learning (DRL). Currently, little is known regarding hyperparameter optimization for DRL algorithms. Given that DRL algorithms are computationally

The Graphics Processing Unit (GPU) is now used extensively for general purpose GPU programming (GPGPU), allowing for greater exploitation of the multi-core model across many application domains. This is particularly true in cloud/edge/fog computing, where multiple GPU enabled servers support many different end user services. This move away from the naturally parallel domain of graphics can incur significant

Deduplication is one of the major storage optimisation techniques for Virtual Machines (VMs) in cloud environment. Usually, hashing of blocks helps in identifying duplicate data blocks. This paper proposes a novel deduplication approach, QuickDedup that reduces the overall deduplication time, metadata overhead and the number of hash computations, and subsequent comparisons for the VM disk images. In

This paper introduces a platform to support serverless computing for scalable event-driven data processing that features a multi-level elasticity approach combined with virtualization of GPUs. The platform supports the execution of applications based on Docker containers in response to file uploads to a data storage in order to perform the data processing in parallel. This is managed by an elastic

In the age of the information explosion, the energy demand for cloud data centers has increased markedly; hence, reducing the energy consumption of cloud data centers is essential. Dynamic virtual machine VM consolidation, as one of the effective methods for reducing energy energy consumption is extensively employed in large cloud data centers. It achieves the energy reductions by concentrating the

Spectral clustering is widely used in data mining, machine learning and other fields. It can identify the arbitrary shape of a sample space and converge to the global optimal solution. Compared with the traditional k-means algorithm, the spectral clustering algorithm has stronger adaptability to data and better clustering results. However, the computation of the algorithm is quite expensive. In this

The performance of parallel algorithms is often inconsistent with their preliminary theoretical analyses. Indeed, the difference is increasing between the ability to theoretically predict the performance of a parallel algorithm and the results measured in practice. This is mainly due to the accelerated development of advanced parallel architectures, whereas there is still no agreed model for parallel

Hypercube and folded hypercube are the most fundamental interconnection networks for the attractive topological properties. We assume for any distinct vertices u,v∈V,κ(u,v) defined as local connectivity of u and v, is the maximum number of independent (u,v)-paths in G. Similarly, λ(u,v) is local edge connectivity of u,v. For some t∈[1,D(G)],∀u,v∈V,u≠v, and d(u,v)=t, if κ(u,v)(orλ(u,v))=min{d(u),d(v)}

Nowadays, more than ever, digital forensics activities are involved in any criminal, civil or military investigation and represent a fundamental tool to support cyber-security. Investigators use a variety of techniques and proprietary software forensics applications to examine the copy of digital devices, searching hidden, deleted, encrypted, or damaged files or folders. Any evidence found is carefully

The main contribution of this paper is to show efficient implementations of the convolution-pooling in the GPU, in which the pooling follows the multiple convolution. Since the multiple convolution and the pooling operations are performed alternately in earlier stages of many Convolutional Neural Networks (CNNs), it is very important to accelerate the convolution-pooling. Our new GPU implementation

Modern applications generally need a large volume of computation and communication to fulfill the goal. These applications are often implemented on multiprocessor systems to meet the requirements in computing capacity and communication bandwidth, whereas, how to obtain a good or even the optimal performance on such systems remains a challenge. When tasks of the application are mapped onto different

Three parallel sorting applications and two list output protocols for the first phase of an external sort execute on a fine-grained many-core processor array that contains no algorithm-specific hardware acting as a co-processor with a variety of array sizes. Results are generated using a cycle-accurate model based on measured data from a fabricated many-core chip, and simulated for different processor

With the advent of the Big data, the scalability of the machine learning algorithms has become more crucial than ever before. Furthermore, Feature selection as an essential preprocessing technique can improve the performance of the learning algorithms in confront with large-scale dataset by removing the irrelevant and redundant features. Owing to the lack of scalability, most of the classical feature

Live migration of virtual machines (VMs) has become an extremely powerful tool for cloud data center management and provides significant benefits of seamless VM mobility among physical hosts within a data center or across multiple data centers without interrupting the running service. However, with all the enhanced techniques that ensure a smooth and flexible migration, the down-time of any VM during

Nowadays, Big Data security processes require mining large amounts of content that was traditionally not typically used for security analysis in the past. The RSA algorithm has become the de facto standard for encryption, especially for data sent over the internet. RSA takes its security from the hardness of the Integer Factorisation Problem. As the size of the modulus of an RSA key grows with the

In this work we have implemented a novel Linear Algebra Library on top of the task-based runtime OmpSs-2. We have used some of the most advanced OmpSs-2 features; weak dependencies and regions, together with the final clause for the implementation of auto-tunable code for the BLAS-3 trsm routine and the LAPACK routines npgetrf and npgesv. All these implementations are part of the first prototype of

In this paper we survey a number of interesting applications of blockchain technology not related to cryptocurrencies. As a matter of fact, after an initial period of application to cryptocurrencies and to the financial world, blockchain technology has been successfully exploited in many other different scenarios, where its unique features allowed the definition of innovative and sometimes disruptive

Encryption is an important technique to improve information security for many real-world applications. The Advanced Encryption Standard (AES) is a widely-used efficient cryptographic algorithm. Although AES is fast both in software and hardware, it is time-consuming to do data encryption especially for large amount of data. Therefore, it is a lasting effort to accelerate AES operations. This paper

Biological data is prone to grow exponentially, which consumes more resources, time and manpower. Parallelization of algorithms could reduce overall execution time. There are two main challenges in parallelizing computational methods. (1) Biological data is multi-dimensional in nature. (2). Parallel algorithms reduces execution time, but with the penalty of reduced prediction accuracy. This research

An algorithm to partition structured multi-block hexahedral grids for a load balanced assignment of the partitions to a given number of bins is presented. It uses a balanced hierarchical cut tree data structure to partition the structured blocks into structured partitions. The refinement of the cut tree attempts to generate equally shaped partitions with a low amount of additional surface. A multi-block

We have implemented the computation of Coulomb interactions in particle systems using the performance portable C++ framework Kokkos. For the computation of the electrostatic interactions in particle systems we used an Ewald summation. This implementation we consider as a basis for a performance portability study. As target architectures we used Intel CPUs, including Intel Xeon Phi, as well as Nvidia

Many applications in high performance computing are designed based on underlying performance and execution models. While these models could successfully be employed in the past for balancing load within and between compute nodes, modern software and hardware increasingly make performance predictability difficult if not impossible. Consequently, balancing computational load becomes much more difficult

Micro-core architectures combine many low memory, low power computing cores together in a single package. These are attractive for use as accelerators but due to limited on-chip memory and multiple levels of memory hierarchy, the way in which programmers offload kernels needs to be carefully considered. In this paper we use Python as a vehicle for exploring the semantics and abstractions of higher

Linked lists and other list-based sets are some of the most ubiquitous data structures in computer science. They are useful in their own right and are frequently used as building blocks in other data structures. A linked list can be “unrolled” to combine multiple keys in each node; this improves storage density and overall performance. This organization also allows an operation to skip over nodes which

Bayesian Sequential Partitioning (BSP) is a statistically effective density estimation method to comprehend the characteristics of a high dimensional data space. The intensive computation of the statistical model and the counting of enormous data have caused serious design challenges for BSP to handle the growing volume of the data. This paper proposes a high performance design of BSP by leveraging

An edge switch is an operation on a graph (or network) where two edges are selected randomly and one of their end vertices are swapped with each other. Edge switch operations have important applications in graph theory and network analysis, such as in generating random networks with a given degree sequence, modeling and analyzing dynamic networks, and in studying various dynamic phenomena over a network

Large-scale compute clusters of heterogeneous nodes equipped with multi-core CPUs and GPUs are getting increasingly popular in the scientific community. However, such systems require a combination of different programming paradigms making application development very challenging. In this article we introduce libWater, a library-based extension of the OpenCL programming model that simplifies the development

A graph is chordal if every cycle of length greater than three contains an edge between non-adjacent vertices. Chordal graphs are of interest both theoretically, since they admit polynomial time solutions to a range of NP-hard graph problems, and practically, since they arise in many applications including sparse linear algebra, computer vision, and computational biology. A maximal chordal subgraph

Several recent methods have been proposed to obtain significant speed-ups in MRI image reconstruction by leveraging the computational power of GPUs. Previously, we implemented a GPU-based image reconstruction technique called the Illinois Massively Parallel Acquisition Toolkit for Image reconstruction with ENhanced Throughput in MRI (IMPATIENT MRI) for reconstructing data collected along arbitrary

Genome resequencing with short reads generated from pyrosequencing generally relies on mapping the short reads against a single reference genome. However, mapping of reads from multiple reference genomes is not possible using a pairwise mapping algorithm. In order to align the reads w.r.t each other and the reference genomes, existing multiple sequence alignment(MSA) methods cannot be used because

In this paper we present efficient algorithms for sorting on the Parallel Disks Model (PDM). Numerous asymptotically optimal algorithms have been proposed in the literature. However many of these merge based algorithms have large underlying constants in the time bounds, because they suffer from the lack of read parallelism on PDM. The irregular consumption of the runs during the merge affects the read

We present a multi-heuristic evolutionary task allocation algorithm to dynamically map tasks to processors in a heterogeneous distributed system. It utilizes a genetic algorithm, combined with eight common heuristics, in an effort to minimize the total execution time. It operates on batches of unmapped tasks and can preemptively remap tasks to processors. The algorithm has been implemented on a Java

Tomographic imaging and computer simulations are increasingly yielding massive datasets. Interactive and exploratory visualizations have rapidly become indispensable tools to study large volumetric imaging and simulation data. Our scalable isosurface visualization framework on commodity off-the-shelf clusters is an end-to-end parallel and progressive platform, from initial data access to the final

Computational acceleration on graphics processing units (GPUs) can make advanced magnetic resonance imaging (MRI) reconstruction algorithms attractive in clinical settings, thereby improving the quality of MR images across a broad spectrum of applications. This paper describes the acceleration of such an algorithm on NVIDIA's Quadro FX 5600. The reconstruction of a 3D image with 128(3) voxels achieves

High-dimensional problems arising from robot motion planning, biology, data mining, and geographic information systems often require the computation of k nearest neighbor (knn) graphs. The knn graph of a data set is obtained by connecting each point to its k closest points. As the research in the above-mentioned fields progressively addresses problems of unprecedented complexity, the demand for computing